AI Solution Architect

At Cleco, we're not just poweringlives-we're powering a cleaner, smarter future for Louisiana.With bold investments in innovativeenergysolutions, we're transforminghow we power ourcommunities: smarter, cleaner, and more sustainable.This is a long-term commitmentto our people and our communities because our future-and the future of generations to come-depends on it. If you're ready to make an impact where it matters most, join us at Cleco-where we're Energizing Your Tomorrow.

The AI Solution Architect II is responsible for the architecture, engineering, and production operationalization of enterprise AI solutions, ensuring they are scalable, secure, and performant. This role leverages appropriate methodologies, frameworks, tools, and platforms to architect and implement AI platforms, pipelines, and solution patterns that meet business objectives. The AI Architect ensures standardization, reuse, integration, quality, and lifecycle manageability of AI architectures through close coordination with technology architects, data architects, and enterprise architects. The position involves deep technical evaluation of AI/ML models and platforms, integration into Cleco's enterprise IT landscape, collaboration with cybersecurity teams, and support for testing and quality assurance processes.

Key Responsibilities

• Champions a corporate culture that emphasizes transparency, integrity, safety, environmental responsibility, employee development, diversity and inclusion, customer service, and operational excellence.
• Architect and engineer end-to-end AI solutions, platforms, and pipelines to address specific business needs, ensuring scalability, security, and operational readiness; provide implementation guidance upon delivery.
• Define and govern AI architecture standards, platforms, and reference frameworks, guiding decisions on governance, methodologies, and operational processes to ensure safety and compliance at scale.
• Design and implement AI solutions across cloud and hybrid environments, optimizing for performance, cost-efficiency, and resource utilization.
• Evaluate AI/ML models and system architectures, including scalability, computational efficiency, accuracy, robustness, and detection of model drift or anomalies.
• Ensure seamless integration of AI solutions into Cleco's enterprise IT ecosystem, including SaaS, PaaS, and IaaS environments, in alignment with enterprise architecture and integration standards.
• Partner with cybersecurity, privacy, and risk teams to identify vulnerabilities, implement security controls, and conduct risk assessments for AI systems and data pipelines.
• Streamline cross-functional collaboration by coordinating architects, engineers, and business stakeholders to identify and prioritize AI requirements.
• Navigate technical complexity by developing scenarios and recommendations for selecting AI platforms, frameworks, and tools that complement one another and meet business objectives.
• Enable AI testing, validation, and release readiness, including designing test strategies, validating system behavior, supporting defects analysis, and enabling CI/CD and ModelOps pipelines for AI deployments.
• Own and maintain AI architecture documentation, standards, integration patterns, and lifecycle artifacts, ensuring reusability, compliance, and auditability across AI solutions.

Qualifications

Required Education, Skills & Experience

• A bachelor's degree in computer engineering, computer science, software engineering, artificial intelligence, mathematics, or a related quantitative or natural science discipline is required; a master's degree or Ph.D. is highly preferred or a minimum of five years of experience building and operating production-grade AI/ML systems, including principal-level ownership of MLOps pipelines, model deployment, monitoring, and cloud-native platform architecture in distributed enterprise environments.
• A minimum of three years experience building and operating production-grade AI/ML systems, including principal-level ownership of MLOps pipelines, model deployment, monitoring, and cloud-native platform architecture in distributed enterprise environments.
• Proven experience working with large language models and foundation models, including designing architectures optimized for cost efficiency, scalability, and operational performance.
• Experience architecting AI solutions incorporating advanced techniques such as natural language processing (NLP), vector databases, knowledge graphs, and retrieval-augmented generation (RAG) architectures.
• Skilled in selecting and architecting AI frameworks (e.g., TensorFlow, PyTorch, Hugging Face), cloud platforms (Azure, AWS), and orchestration technologies (Docker, Kubernetes) for scalable enterprise AI deployments.
• Ability to apply AI system testing and validation techniques (e.g., pairwise testing, A/B testing, back-to-back testing) across operating systems and deployment environments.
• Advanced understanding of enterprise integration architectures, APIs, data pipelines, and event-driven systems.
• Excellent written and verbal communication skills, with the ability to explain complex AI architectures and trade-offs to technical and non-technical stakeholders.
• Strong documentation skills with the ability to produce diagrams, demos and technical artifacts that make AI architectures comprehensible and actionable
• Strong working knowledge of ModelOps, AI engineering, DevOps, and MLOps practices, including CI/CD pipelines, monitoring, and production support models.
• Progression to this level is strictly restricted based on critical individual capabilities and business requirements; must be supported by market survey data.

Key Competencies

BEHAVIORAL

• Building Partnerships
• Leading Teams
• Business Acumen
• Communication
• Courage
• Building Self-Insight
• Driving for Results
• Energizing the Organization
• Driving Execution
• Building Trusting Relationships
• Driving Innovation
• Planning and Organizing
• Safety
• Establishing Strategic Direction

TECHNICAL

• Analytical skills
• Compliance
• Operations Resource Management
• Operations Risk Management
• Technology Integration
• Data and analytics fluency
• AI/ML lifecycle knowledge

May perform other duties as assigned.

Salary dependent on experience, skills, education, and training.